Manage Data Models in Looker
Earned nov. 8, 2022 EST
Delivery Shadowing
Earned nov. 8, 2022 EST
Case Studies
Earned nov. 7, 2022 EST
Technology + Beyond the UI
Earned nov. 7, 2022 EST
Technology + Within the UI
Earned nov. 7, 2022 EST
Table Calculations, Pivots, and Visualizations
Earned nov. 7, 2022 EST
Building Reports in Looker
Earned nov. 7, 2022 EST
Liquid Templates and Parameters
Earned nov. 6, 2022 EST
Extends to Keep LookML DRY
Earned nov. 6, 2022 EDT
Admin Roles and Folder Access
Earned nov. 5, 2022 EDT
Version Control and Caching
Earned nov. 5, 2022 EDT
The Modern Data Platform and LookML
Earned nov. 4, 2022 EDT
Driving Data Culture and Designing Dashboards
Earned nov. 3, 2022 EDT
Achieving Business Outcomes with Looker
Earned oct. 6, 2022 EDT
Looker Explained
Earned août 28, 2022 EDT
Traitement des données sans serveur avec Dataflow : opérations
Earned juin 21, 2022 EDT
Créer des pipelines de données en batch sur Google Cloud
Earned juin 16, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Français
Earned juin 9, 2022 EDT
Complete the intermediate Manage Data Models in Looker skill badge to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.
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By the end of this course, you should feel confident employing technical concepts to fulfill business requirements and be familiar with common complex design patterns.
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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
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Develop technical skills beyond LookML along with basic administration for optimizing Looker instances
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This course reviews the processes for creating table calculations, pivots and visualizations
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This course is designed for Looker users who want to create their own ad-hoc reports. It assumes experience of everything covered in our Get Started with Looker course (logging in, finding Looks & dashboards, adjusting filters, and sending data)
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In this course you will discover Liquid, the templating language invented by Shopify and explore how it can be used in Looker to create dynamic links, content, formatting, and more.
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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.
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This course is designed to teach you about roles, permission sets and model sets. These are areas that are used together to manage what users can do and what they can see in Looker.
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This course aims to introduce you to the basic concepts of Git: what it is and how it's used in Looker. You will also develop an in-depth knowledge of the caching process on the Looker platform, such as why they are used and why they work
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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.
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This course provides an iterative approach to plan, build, launch, and grow a modern, scalable, mature analytics ecosystem and data culture in an organization that consistently achieves established business outcomes. Users will also learn how to design and build a useful, easy-to-use dashboard in Looker. It assumes experience with everything covered in our Getting Started with Looker and Building Reports in Looker courses.
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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.
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By the end of this course, you should be able to articulate Looker's value propositions and what makes it different from other analytics tools in the market. You should also be able to explain how Looker works, and explain the standard components of successful service delivery.
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Dans le dernier volet de la série de cours sur Dataflow, nous allons présenter les composants du modèle opérationnel de Dataflow. Nous examinerons les outils et techniques permettant de résoudre les problèmes et d'optimiser les performances des pipelines. Nous passerons ensuite en revue les bonnes pratiques en matière de test, de déploiement et de fiabilité pour les pipelines Dataflow. Nous terminerons par une présentation des modèles, qui permettent de faire évoluer facilement les pipelines Dataflow pour les adapter aux organisations comptant des centaines d'utilisateurs. Ces leçons vous aideront à vous assurer que votre plate-forme de données est stable et résiliente face aux imprévus.
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Les pipelines de données s'inscrivent généralement dans l'un des paradigmes EL (extraction et chargement), ELT (extraction, chargement et transformation) ou ETL (extraction, transformation et chargement). Ce cours indique quel paradigme utiliser pour le traitement de données par lot en fonction du contexte. Il présente également plusieurs technologies Google Cloud de transformation des données, y compris BigQuery, l'exécution de Spark sur Dataproc, les graphiques de pipelines dans Cloud Data Fusion et le traitement des données sans serveur avec Dataflow. Les participants mettront en pratique les connaissances qu'ils auront acquises en créant des composants de pipelines de données sur Google Cloud à l'aide de Qwiklabs.
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Ce cours présente les produits et services Google Cloud pour le big data et le machine learning compatibles avec le cycle de vie "des données à l'IA". Il explore les processus, défis et avantages liés à la création d'un pipeline de big data et de modèles de machine learning avec Vertex AI sur Google Cloud.
Manage Data Models in Looker
Earned nov. 8, 2022 EST
Delivery Shadowing
Earned nov. 8, 2022 EST
Case Studies
Earned nov. 7, 2022 EST
Technology + Beyond the UI
Earned nov. 7, 2022 EST
Technology + Within the UI
Earned nov. 7, 2022 EST
Table Calculations, Pivots, and Visualizations
Earned nov. 7, 2022 EST
Building Reports in Looker
Earned nov. 7, 2022 EST
Liquid Templates and Parameters
Earned nov. 6, 2022 EST
Extends to Keep LookML DRY
Earned nov. 6, 2022 EDT
Admin Roles and Folder Access
Earned nov. 5, 2022 EDT
Version Control and Caching
Earned nov. 5, 2022 EDT
The Modern Data Platform and LookML
Earned nov. 4, 2022 EDT
Driving Data Culture and Designing Dashboards
Earned nov. 3, 2022 EDT
Achieving Business Outcomes with Looker
Earned oct. 6, 2022 EDT
Looker Explained
Earned août 28, 2022 EDT
Traitement des données sans serveur avec Dataflow : opérations
Earned juin 21, 2022 EDT
Créer des pipelines de données en batch sur Google Cloud
Earned juin 16, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals - Français
Earned juin 9, 2022 EDT